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Reconstruction of the El Niño attractor with neural networks

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Abstract

Based on a combined data set of sea surface temperature, zonal surface wind stress and upper ocean heat content the dynamics of the El Niño phenomenon is investigated. In a reduced phase space spanned by the first four EOFs two different stochastic models are estimated from the data. A nonlinear model represented by a simulated neural network is compared with a linear model obtained with the principal oscillation pattern (POP) analysis. While the linear model is limited to damped oscillations onto a fix point attractor, the nonlinear model recovers a limit cycle attractor. This indicates that the real system is located above the bifurcation point in parameter space supporting self-sustained oscillations. The results are discussed with respect to consistency with current theory.

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Authors and Affiliations

  1. Department of Geology, University of Bremen, Postfach, D-28334, 330440, Bremen, Germany

    B Grieger

  2. Max-Planck-Institute of Meteorology, Bundesstr. 55, D-20146, Hamburg, Germany

    M Latif

Authors
  1. B Grieger
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  2. M Latif
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Grieger, B., Latif, M. Reconstruction of the El Niño attractor with neural networks. Climate Dynamics 10, 267–276 (1994). https://doi.org/10.1007/BF00228027

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  • DOI: https://doi.org/10.1007/BF00228027

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